import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


__a    :	List[Any]         =    get_tests_dir() + """/test_data/fsmt/fsmt_val_data.json"""
with io.open(filename, """r""", encoding="""utf-8""") as f:
		__a    :	Optional[Any]         =    json.load(f)




@require_torch
class   _UpperCamelCase (		unittest.TestCase					):


			"""simple docstring"""






			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__		)	->   int:

							'''simple docstring'''


							return FSMTTokenizer.from_pretrained(lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__		)	->   Dict:

							'''simple docstring'''


							__lowercase					     =				FSMTForConditionalGeneration.from_pretrained(lowerCAmelCase__		).to(lowerCAmelCase__		)
							if torch_device == "cuda":
											model.half()
							return model





			@parameterized.expand(
			    [
			        ['''en-ru''', 26.0],
			        ['''ru-en''', 22.0],
			        ['''en-de''', 22.0],
			        ['''de-en''', 29.0],
			    ]		)
			@slow
			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   Union[str, Any]:

							'''simple docstring'''


							__lowercase					     =				F"facebook/wmt19-{pair}"
							__lowercase					     =				self.get_tokenizer(lowerCAmelCase__		)
							__lowercase					     =				self.get_model(lowerCAmelCase__		)

							__lowercase					     =				bleu_data[pair]['''src''']
							__lowercase					     =				bleu_data[pair]['''tgt''']

							__lowercase					     =				tokenizer(lowerCAmelCase__ ,  return_tensors='''pt''' ,  truncation=lowerCAmelCase__ ,  padding='''longest'''		).to(lowerCAmelCase__		)
							__lowercase					     =				model.generate(
							    input_ids=batch.input_ids ,  num_beams=8 ,  )
							__lowercase					     =				tokenizer.batch_decode(
							    lowerCAmelCase__ ,  skip_special_tokens=lowerCAmelCase__ ,  clean_up_tokenization_spaces=lowerCAmelCase__		)
							__lowercase					     =				calculate_bleu(lowerCAmelCase__ ,  lowerCAmelCase__		)
							print(lowerCAmelCase__		)
							self.assertGreaterEqual(scores['''bleu'''] ,  lowerCAmelCase__		)
import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class   _UpperCamelCase (		_UpperCAmelCase		,_UpperCAmelCase					):


			"""simple docstring"""






			@register_to_config
			def __init__(  self ,  *,
			    lowerCAmelCase__ = 4 ,  lowerCAmelCase__ = 7_68 ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  )	->   Dict:

							'''simple docstring'''


							super().__init__()

							__lowercase					     =				nn.Parameter(torch.zeros(lowerCAmelCase__		)		)

							# parameters for additional clip time embeddings
							__lowercase					     =				nn.Linear(lowerCAmelCase__ ,  lowerCAmelCase__		)
							__lowercase					     =				nn.Linear(lowerCAmelCase__ ,  lowerCAmelCase__		)

							# parameters for encoder hidden states
							__lowercase					     =				clip_extra_context_tokens
							__lowercase					     =				nn.Linear(
							    lowerCAmelCase__ ,  self.clip_extra_context_tokens * cross_attention_dim		)
							__lowercase					     =				nn.Linear(lowerCAmelCase__ ,  lowerCAmelCase__		)
							__lowercase					     =				nn.LayerNorm(lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  *, lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   List[str]:

							'''simple docstring'''


							if do_classifier_free_guidance:
											# Add the classifier free guidance embeddings to the image embeddings
											__lowercase					     =				image_embeddings.shape[0]
											__lowercase					     =				self.learned_classifier_free_guidance_embeddings.unsqueeze(0		)
											__lowercase					     =				classifier_free_guidance_embeddings.expand(
											    lowerCAmelCase__ ,  -1		)
											__lowercase					     =				torch.cat([classifier_free_guidance_embeddings, image_embeddings] ,  dim=0		)

							# The image embeddings batch size and the text embeddings batch size are equal
							assert image_embeddings.shape[0] == prompt_embeds.shape[0]

							__lowercase					     =				prompt_embeds.shape[0]

							# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
							# adding CLIP embeddings to the existing timestep embedding, ...
							__lowercase					     =				self.embedding_proj(lowerCAmelCase__		)
							__lowercase					     =				self.clip_image_embeddings_project_to_time_embeddings(lowerCAmelCase__		)
							__lowercase					     =				time_projected_image_embeddings + time_projected_prompt_embeds

							# ... and by projecting CLIP embeddings into four
							# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
							__lowercase					     =				self.clip_extra_context_tokens_proj(lowerCAmelCase__		)
							__lowercase					     =				clip_extra_context_tokens.reshape(lowerCAmelCase__ ,  -1 ,  self.clip_extra_context_tokens		)
							__lowercase					     =				clip_extra_context_tokens.permute(0 ,  2 ,  1		)

							__lowercase					     =				self.encoder_hidden_states_proj(lowerCAmelCase__		)
							__lowercase					     =				self.text_encoder_hidden_states_norm(lowerCAmelCase__		)
							__lowercase					     =				torch.cat([clip_extra_context_tokens, text_encoder_hidden_states] ,  dim=1		)

							return text_encoder_hidden_states, additive_clip_time_embeddings